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Job Description
AI Solution Architect
Job Location:  Lodz
Location Flexibility:  Primary Location Only
Req Id:  2919
Posting Start Date:  10/30/25

Responsibilities:

  • Co‑create AI vision, KPI tree, and prioritized use‑case portfolio with business leaders. 

  • Translate strategy to a delivery roadmap and budget, with explicit risk and dependency plans. 

  • Delivery leadership 

  • Lead cross‑functional pods (data, platform, app, safety, SRE) from discovery through production. 

  • Design A/B and canary rollout strategies; enforce guardrails and incident playbooks. 

  • AI system engineering 

  • Architect and guide implementation of LLM/RAG/agent solutions, including retrieval quality, prompt/policy engineering, guardrails, and evaluation harnesses. 

  • Drive observability (tracing, safety counters, cost telemetry) and SLO compliance. 

  • Governance and security 

  • Stand up policy‑as‑code, model/prompt versioning, access controls, data residency, and vendor risk assessments. 

  • Chair or collaborate with the governance board; run review gates. 

  • Stakeholder engagement 

  • Communicate simply and often; convert ambiguity into decisions; manage expectations. 

  • Run demos, evidence‑based decisions, and post‑incident reviews. 

  • Talent enablement 

  • Mentor teams on AIOps/SRE practices; cultivate champions; reduce burden through automation. 

Must‑have skills:

  • Leadership and ownership 

  • Operates with high autonomy, bias to action, and accountability for outcomes. 

  • Proven ability to align executives and guide cross‑functional teams without formal authority. 

  • Communication and influence 

  • Excellent written and verbal communication and meeting facilitation skills. 

  • Translates technical topics (LLMs, safety, SOs) into business terms and tradeoffs. 

  • Product and delivery thinking 

  • Evidence‑driven decisions; comfort with A/B testing, canary rollouts, and ROI models. 

  • Governance and security mindset 

  • Practical understanding of data governance, privacy, and AI safety guardrails; policy‑as‑code. 

  • Hands‑on AI systems integration 

  • Experience integrating GenAI (LLMs/RAG/KG/agents) into real products with telemetry, guardrails, and rollback. 

  • AIOps and reliability fundamentals 

  • SLI/SLO design, error budgets, incident management, observability, CI/CD for prompts/policies/indexes. 

  • Manufacturing/OT/IoT/Edge AI experience; familiarity with device data and shop‑floor constraints. 

  • Microsoft Azure: Azure OpenAI, Cognitive Search, API Management, App Service/AKS, Functions, Event Hubs, Key Vault, Monitor; Azure ML or equivalent. 

Nice‑to‑have skills 

  • SAP ecosystem awareness (SAP/S4H processes and integration points for AI governance). 

  • Data and knowledge systems 

  • Knowledge graphs/ontologies, hybrid retrieval (vector + keyword), embeddings, data contracts. 

  • Safety and compliance 

  • Red‑teaming methods, PII/PHI handling, content moderation pipelines, audit trails. 

  • Cost and performance engineering 

  • token/call cost controls. 

Experience profile 

  • 7–12+ years in software/AI product delivery with 3+ years leading cross‑functional initiatives. 

  • Track record of shipping AI or data‑intensive systems to production in enterprise settings. 

  • Demonstrated practice of Site Reliability Engineering (SRE)/AIOps concepts (SLOs, incident response, observability). 
Relocation Supported: 
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